Abstract
Most plant species have unique leaves which differ from each other by characteristics such as the shape, colour, texture and the margin. Details of the leaf margin are an important feature in comparative plant biology, although they have largely overlooked in automated methods of classification. This paper presents a new method for classifying plants according to species, using only the leaf margins. This is achieved by utilizing the dynamic time warping (DTW) algorithm. A margin signature is extracted and the leaf’s insertion point and apex are located. Using these as start points, the signatures are then compared using a version of the DTW algorithm. A classification accuracy of over 90% is attained on a dataset of 100 different species.
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References
Backes, A.R., Bruno, O.M.: Plant Leaf Identification Using Multi-scale Fractal Dimension. In: Foggia, P., Sansone, C., Vento, M. (eds.) ICIAP 2009. LNCS, vol. 5716, pp. 143–150. Springer, Heidelberg (2009)
Casanova, D., de Mesquita Sá Junior, J.J., Bruno, O.M.: Plant leaf identification using Gabor wavelets. International Journal of Imaging Systems and Technology 19, 236–243 (2009)
Clark, J.Y.: Plant identification from characters and measurements using artificial neural networks. In: MacLeod, N. (ed.) Automated Taxon Identification in Systematics: Theory, Approaches and Applications, pp. 207–224. CRC (2007)
Clark, J.Y.: Neural networks and cluster analysis for unsupervised classification of cultivated species of Tilia (Malvaceae). Botanical Journal of the Linnean Society 159, 300–314 (2009)
Cope, J.S., Remagnino, P., Barman, S., Wilkin, P.: Plant Texture Classification Using Gabor Co-occurrences. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammound, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010. LNCS, vol. 6454, pp. 669–677. Springer, Heidelberg (2010)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV 2004, pp. 1–22 (2004)
Ellis, B., Daly, D.C., Hickey, L.J., Johnson, K.R., Mitchell, J.D., Wilf, P., Wing, S.L.: Manual of Leaf Architecture. Cornell University Press (2009)
Hearn, D.J.: Shape analysis for the automated identification of plants from images of leaves. Taxon 58, 934–954 (2009)
Jensen, R.J., Ciofani, K.M., Miramontes, L.C.: Lines, outlines, and landmarks: Morphometric analyses of leaves of Acer rubrum, Acer Saccharinum (Aceraceae) and their Hybrid. Taxon 51(3), 475–492 (2002)
McLellan, T., Endler, J.A.: The relative success of some methods for measuring and describing the shape of complex objects. Systematic Biology 47, 264–281 (1998)
Pauwels, E.J., de Zeeum, P.M., Ranguelova, E.B.: Computer-assisted tree taxonomy by automated image recognition. Engineering Applications of Artificial Intelligence 22(1), 26–31 (2009)
Plotze, R.D., Falvo, M., Padua, J.G., Bernacci, L.C., Vieira, M.L.C., Oliveira, G.C.X., Martinez, O.: Leaf shape analysis using the multiscale Minkowski fractal dimension, a new morphometric method: A study with Passiflora (Passifloraceae). Canadian Journal of Botany 83(3), 287–301 (2005)
Royer, D.L., Wilf, P.: Why do toothed leaves correlate with cold climates? Gas exchange at leaf margins provides new insights into a classic paleotemperature proxy. International Journal of Plant Sciences 167(1), 11–18 (2006)
Royer, D.L., Wilf, P., Janesko, D.A., Kowalski, E.A., Dilcher, D.L.: Correlations of climate and plant ecology to leaf size and shape: potential proxies for the fossil record. American Journal of Botany 92(7), 1141–1151 (2005)
Rumpunen, K., Bartish, I.V.: Comparison of differentiation estimates based on morphometric and molecular data, exemplified by various leaf shape descriptors and RAPDs in the genus Chaenomeles. Taxon 51, 69–82 (2002)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing 26(1), 43–49 (1978)
Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intelligent Data Analysis 11(5), 561–580 (2007)
Sempena, S., Maulidevi, N.U., Aryan, P.R.: Human action recognition using dynamic time warping. In: International Conference on Electrical Engineering and Informatics. IEEE (2011)
Turkan, M., Dulek, B., Onaran, I., Cetin, A.: Human face detection in video using edge projections. In: Visual Information Processing. SPIE (2006)
Wang, Z., Chi, Z., Dagan, F.: Shape based leaf image retrieval. Vision, Image And Signal Processing 150, 34–43 (2003)
Ye, L., Keogh, E.: Time series shapelets: A new primitive for data mining. In: IEEE International Conference on Knowledge Discovery and Data Mining, pp. 947–956. ACM (2009)
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Cope, J.S., Remagnino, P. (2012). Classifying Plant Leaves from Their Margins Using Dynamic Time Warping. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2012. Lecture Notes in Computer Science, vol 7517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33140-4_23
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DOI: https://doi.org/10.1007/978-3-642-33140-4_23
Publisher Name: Springer, Berlin, Heidelberg
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